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Previous issue (2021. Vol. 11, no. 4)

Modelling and Data Analysis

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2219-3758

ISSN (online): 2311-9454

DOI: https://doi.org/10.17759/mda

License: CC BY-NC 4.0

Published since 2011

Published 4 times a year

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The Concept of an Adaptive Trainer and Assessing Its Effectiveness in a Mathematical Application 65

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Kuravsky L.S.
Doctor of Engineering, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-3375-8446
e-mail: l.s.kuravsky@gmail.com

Pominov D.A.
Research Scholar, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-1321-3713
e-mail: pominovda@mgppu.ru

Yuryev G.A.
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-2960-6562
e-mail: g.a.yuryev@gmail.com

Yuryeva N.E.
PhD in Engineering, Research Fellow, Center for Computer Science Faculty for Psychological Research, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0003-1419-876X
e-mail: yurieva.ne@gmail.com

Safronova M.A.
PhD in Psychology, Dean of the Faculty of Psychology of Education, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-3597-6375
e-mail: mariasaf@gmail.com

Kulanin Y.D.
PhD in Physics and Matematics, Professor, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0001-6093-7012
e-mail: lucas03@mail.ru

Antipova S.N.
Deputy Dean for Extra-Curricular Work, Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0001-6642-7953
e-mail: antipovasn@mgppu.ru

Abstract
Presented is a mathematical model of the self-learning adaptive trainer intended for adaptive learning and providing task selection. The approach in question is an alternative to the adaptive technologies based on the Item Response Theory. Possibility to take into account temporal dynamics of solution ability as well as smaller number of tasks that must be performed by a subject to provide the given results are among the features of the methods in use. To assess the effectiveness of the adaptive trainer concept under consideration, its web-implementation intended for training school students to solve mathematical tasks covered by the school curriculum was employed. The analysis performed revealed both high efficiency of the adaptive trainer (the mean test rating has increased 1.54 times owing to its use) and proven statistically significant influences of the adaptive training factor on the observed mathematical test results.

Keywords: adaptive learning, Markovian random processes, adaptive trainer, self-learning systems

Column: Data Analysis

DOI: https://doi.org/10.17759/mda.2021110401

Funding. The work was financially supported by the Ministry of Education of the Russian Federation within the framework of State Assignment “Development and practical implementation of an adaptive training model based on the identifiable Markovian processes“ dated 10 December 2021, No. 073–00041–21–10.

For Reference

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